Stego-Image Synthesis Employing Data-Driven Continuous Variable Representations of Cover Images

• Published in: IEEE access 2024, Vol.12, p.146749-146770
• Main Authors: Almuayqil, Saleh Naif, Fadel, Magdy M., Hassan, Mohammed K., Hagras, Esam A. A., Said, Wael
• Format: Article
• Language: English
• Subjects: Adaptation models; Computational modeling; Convolutional neural network; Convolutional neural networks; data hiding; Decoding; Distortion; generative adversarial network; Generative adversarial networks; generative artificial intelligence; generative image synthesis; Generators; Image synthesis; Steganography; Training; variational autoencoder
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3468886

The security of stego-images is a crucial foundation for analyzing steganography algorithms. Recently, steganography has made significant strides in ongoing conflicts with steganalysis. In order to increase the security of stego-images, steganography must be able to evade detection using steganalysis methods. Secret information is typically hidden using traditional embedding-based steganography, which inevitably leaves traces of the modifications that can be found using more sophisticated machine-learning-based steganalysis techniques. Steganography without embedding (SWE) outperforms machine-learning-based steganalysis techniques because it does not require alteration of the data of the cover image. A novel image SWE method based on deep convolutional generative adversarial networks (GANs) is proposed to synthesize stego-images led by embedded text. The variational autoencoder (VAE) in the GAN model is utilized to synthesize the stego-image, based on interpolating the secret text in a continuous variable representation of the cover image. To further improve the framework's performance and shorten processing times, the whale optimization algorithm (WOA) is used to identify the optimal VAE structure. When creating a stego-image, no embedding or modification procedures are required, and after training, a different convolutional neural network (CNN) known as the extractor can correctly extract the data from the image. The experimental results revealed that this approach has the advantages of evading detection using innovative deep learning (DL) steganalysis architecture and accurate information extraction.